从心律失常心电图信号中检测心血管疾病,使用人工智能模型与超参数调方法
Gowri Shankar Manivannan1, Harikumar Rajaguru2, Rajanna S1
1Malnad College of Engineering, Hassan, Karnataka, India.
Heliyon
|September 12, 2024
概括
这项研究使用心电图分析增强了心血管疾病检测,通过高级特征选择和超参数调节,提高了高达93.77%的准确性. 最好的性能在ST与NSR检测方面达到98.92%的准确性.
科学领域:
- 心脏病学和生物医学工程
- 机器学习用于医疗保健
- 用于医学诊断的信号处理.
背景情况:
- 心血管疾病 (CVD) 与通过心电图检测到的不规则的心脏电活动有关.
- 自动化心电图分析对于及时检测心律失常至关重要,包括静脉动心 (VT),早发性静脉收缩 (PVC) 和ST变化 (ST).
- 目前的方法需要提高在识别这些关键心脏病的准确性和效率.
研究的目的:
- 通过使用心电图数据,提高特定CVD (VT,PVC,ST) 的检测精度.
- 为了评估各种缩小维度技术的有效性,结合先进的特征选择和超参数优化算法.
- 确定最佳的组合方法,以在心律失常检测中获得优越的分类性能.
主要方法:
- 采用了缩小维度的技术:局部线性嵌入 (LLE),扩散图 (DM) 和拉普拉斯特征 (LE).
- 利用子搜索 (CS) 和子搜索优化 (HSO) 在减少的心电图数据上进行特征选择.
- 使用七个分类器 (GMM,EM,NLR,LR,BDLC,Detrended FA,Firefly) 分类心脏病状况,并使用Adam和网格搜索优化 (GSO) 优化过度参数.
主要成果:
- 特性选择显著提高了准确性:HSO平均准确率为75.39%,超过CS (64.36%) 和无选择 (55.65%).
- 超参数调整进一步提升了性能:Adam优化与HSO特征选择实现了平均准确度为93.77%.
- 使用HSO特征选择和使用Adam调的GMM分类器来减少LLE维度,ST与NSR检测的峰值精度达到98.92%.
结论:
- 集成先进的维度减小,启发式特征选择 (HSO) 和超参数优化 (Adam) 显著提高了基于心电图的心血管疾病检测.
- 带有Adam调的GMM分类器,结合LLE和HSO,在特定的心律失常分类任务中表现出卓越的性能.
- 这种方法为开发更准确,更可靠的用于心血管疾病诊断的自动化系统提供了有希望的途径.
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